READING BARCODES USING DIGITAL CAMERAS ... - KULIS

Figure 5: An image with white background color.
Figure 6: An image with red background color.
The obtained white lines on the resulted image after Canny edge detection and
thresholding will then be separated into several parts. This partitioning is accomplished by
finding neighbor pixels in the image. All neighbor white pixels must stay together, and all
pixels that are not-neighbors must be separated. All of the resulted neighbor pixels will be
stored in the form of neighbor tables as an array. This operation is summarized in algorithm 3
given below.
For all pixels:
1. Select the pixel to be processed.
2. If the pixel is black go to 1. Else go to the next step.
3. If any neighbor table exists, go to step 4. Else construct a new neighbor table entry.
4. For all neighbor tables
a) Search all pixel coordinates in the table to detect any neighbor relationship with
a selected pixel.
b) If any relationship is found, put this pixel in this table and return to step 1.
c) If any relationship is not found, construct a new table and put this pixel in it. Go
to step 1.
Algorithm 3: Constructing neighbor tables.
These tables include all the barcode line places and unnecessary noise. For eliminating
unnecessary noise from the barcode information we have to determine the differences
between them. To do that, at first we must determine the table entries that only includes
barcode line places. The size of tables which include barcode lines must be equal or very
close to each other; because all barcode line heights are equal in an image.
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